{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats # 统计模块\n",
    "import scipy\n",
    "# import pymysql  # 导入数据库模块\n",
    "\n",
    "from datetime import datetime # 时间模块\n",
    "import statsmodels.formula.api as smf  # OLS regression\n",
    "\n",
    "# import pyreadr # read RDS file\n",
    "\n",
    "from matplotlib import style\n",
    "import matplotlib.pyplot as plt  # 画图模块\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "\n",
    "from matplotlib.font_manager import FontProperties # 作图中文\n",
    "from pylab import mpl\n",
    "#mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "\n",
    "#输出矢量图 渲染矢量图\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell # jupyter运行输出的模块\n",
    "#显示每一个运行结果\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "\n",
    "#设置行不限制数量\n",
    "#pd.set_option('display.max_rows',None)\n",
    "\n",
    "#设置列不限制数量\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Preclose</th>\n",
       "      <th>Open</th>\n",
       "      <th>Highest</th>\n",
       "      <th>Lowest</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Money</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td></td>\n",
       "      <td>96.0500</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>95.7900</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>126000</td>\n",
       "      <td>4.940000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>99.98</td>\n",
       "      <td>104.3000</td>\n",
       "      <td>104.3900</td>\n",
       "      <td>99.9800</td>\n",
       "      <td>104.3900</td>\n",
       "      <td>19700</td>\n",
       "      <td>8.400000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>104.39</td>\n",
       "      <td>109.0700</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>103.7300</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>2800</td>\n",
       "      <td>1.600000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>109.13</td>\n",
       "      <td>113.5700</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>109.1300</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>3200</td>\n",
       "      <td>3.100000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>114.55</td>\n",
       "      <td>120.0900</td>\n",
       "      <td>120.2500</td>\n",
       "      <td>114.5500</td>\n",
       "      <td>120.2500</td>\n",
       "      <td>1500</td>\n",
       "      <td>6.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-25</th>\n",
       "      <td>2901.9518</td>\n",
       "      <td>2891.8918</td>\n",
       "      <td>2897.7674</td>\n",
       "      <td>2872.8497</td>\n",
       "      <td>2886.7416</td>\n",
       "      <td>27463950000</td>\n",
       "      <td>2.732820e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-26</th>\n",
       "      <td>2886.7416</td>\n",
       "      <td>2885.9953</td>\n",
       "      <td>2899.1162</td>\n",
       "      <td>2875.3959</td>\n",
       "      <td>2890.8973</td>\n",
       "      <td>27838753600</td>\n",
       "      <td>2.754430e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-29</th>\n",
       "      <td>2890.8973</td>\n",
       "      <td>2889.4726</td>\n",
       "      <td>2898.9512</td>\n",
       "      <td>2878.5825</td>\n",
       "      <td>2891.8453</td>\n",
       "      <td>25689972700</td>\n",
       "      <td>2.600950e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-30</th>\n",
       "      <td>2891.8453</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2865.1493</td>\n",
       "      <td>2879.2996</td>\n",
       "      <td>26247883700</td>\n",
       "      <td>2.694770e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>2879.2996</td>\n",
       "      <td>2877.5409</td>\n",
       "      <td>2940.5927</td>\n",
       "      <td>2876.3009</td>\n",
       "      <td>2938.7493</td>\n",
       "      <td>41272341700</td>\n",
       "      <td>4.188720e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8210 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Preclose       Open    Highest     Lowest      Close  \\\n",
       "Day                                                                 \n",
       "1990-12-19               96.0500    99.9800    95.7900    99.9800   \n",
       "1990-12-20      99.98   104.3000   104.3900    99.9800   104.3900   \n",
       "1990-12-21     104.39   109.0700   109.1300   103.7300   109.1300   \n",
       "1990-12-24     109.13   113.5700   114.5500   109.1300   114.5500   \n",
       "1990-12-25     114.55   120.0900   120.2500   114.5500   120.2500   \n",
       "...               ...        ...        ...        ...        ...   \n",
       "2024-07-25  2901.9518  2891.8918  2897.7674  2872.8497  2886.7416   \n",
       "2024-07-26  2886.7416  2885.9953  2899.1162  2875.3959  2890.8973   \n",
       "2024-07-29  2890.8973  2889.4726  2898.9512  2878.5825  2891.8453   \n",
       "2024-07-30  2891.8453  2885.2152  2885.2152  2865.1493  2879.2996   \n",
       "2024-07-31  2879.2996  2877.5409  2940.5927  2876.3009  2938.7493   \n",
       "\n",
       "                 Volume         Money  \n",
       "Day                                    \n",
       "1990-12-19       126000  4.940000e+05  \n",
       "1990-12-20        19700  8.400000e+04  \n",
       "1990-12-21         2800  1.600000e+04  \n",
       "1990-12-24         3200  3.100000e+04  \n",
       "1990-12-25         1500  6.000000e+03  \n",
       "...                 ...           ...  \n",
       "2024-07-25  27463950000  2.732820e+11  \n",
       "2024-07-26  27838753600  2.754430e+11  \n",
       "2024-07-29  25689972700  2.600950e+11  \n",
       "2024-07-30  26247883700  2.694770e+11  \n",
       "2024-07-31  41272341700  4.188720e+11  \n",
       "\n",
       "[8210 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('D:/ruo-ning/000001.csv')\n",
    "data['Day'] = pd.to_datetime(data['Day'],format='%Y/%m/%d')\n",
    "data.set_index('Day', inplace = True)\n",
    "data.sort_values(by = ['Day'],axis=0, ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Preclose</th>\n",
       "      <th>Open</th>\n",
       "      <th>Highest</th>\n",
       "      <th>Lowest</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Money</th>\n",
       "      <th>Raw_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>647.8700</td>\n",
       "      <td>637.7200</td>\n",
       "      <td>647.7100</td>\n",
       "      <td>630.5300</td>\n",
       "      <td>639.8800</td>\n",
       "      <td>23451800</td>\n",
       "      <td>1.806930e+08</td>\n",
       "      <td>-0.012333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>639.8800</td>\n",
       "      <td>641.9000</td>\n",
       "      <td>655.5100</td>\n",
       "      <td>638.8600</td>\n",
       "      <td>653.8100</td>\n",
       "      <td>42222000</td>\n",
       "      <td>3.069230e+08</td>\n",
       "      <td>0.021770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>653.8100</td>\n",
       "      <td>655.3800</td>\n",
       "      <td>657.5200</td>\n",
       "      <td>645.8100</td>\n",
       "      <td>646.8900</td>\n",
       "      <td>43012300</td>\n",
       "      <td>3.015330e+08</td>\n",
       "      <td>-0.010584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-06</th>\n",
       "      <td>646.8900</td>\n",
       "      <td>642.7500</td>\n",
       "      <td>643.8900</td>\n",
       "      <td>636.3300</td>\n",
       "      <td>640.7600</td>\n",
       "      <td>48748200</td>\n",
       "      <td>3.537580e+08</td>\n",
       "      <td>-0.009476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-09</th>\n",
       "      <td>640.7600</td>\n",
       "      <td>637.5200</td>\n",
       "      <td>637.5500</td>\n",
       "      <td>625.0400</td>\n",
       "      <td>626.0000</td>\n",
       "      <td>50985100</td>\n",
       "      <td>3.985190e+08</td>\n",
       "      <td>-0.023035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-25</th>\n",
       "      <td>2901.9518</td>\n",
       "      <td>2891.8918</td>\n",
       "      <td>2897.7674</td>\n",
       "      <td>2872.8497</td>\n",
       "      <td>2886.7416</td>\n",
       "      <td>27463950000</td>\n",
       "      <td>2.732820e+11</td>\n",
       "      <td>-0.005241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-26</th>\n",
       "      <td>2886.7416</td>\n",
       "      <td>2885.9953</td>\n",
       "      <td>2899.1162</td>\n",
       "      <td>2875.3959</td>\n",
       "      <td>2890.8973</td>\n",
       "      <td>27838753600</td>\n",
       "      <td>2.754430e+11</td>\n",
       "      <td>0.001440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-29</th>\n",
       "      <td>2890.8973</td>\n",
       "      <td>2889.4726</td>\n",
       "      <td>2898.9512</td>\n",
       "      <td>2878.5825</td>\n",
       "      <td>2891.8453</td>\n",
       "      <td>25689972700</td>\n",
       "      <td>2.600950e+11</td>\n",
       "      <td>0.000328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-30</th>\n",
       "      <td>2891.8453</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2885.2152</td>\n",
       "      <td>2865.1493</td>\n",
       "      <td>2879.2996</td>\n",
       "      <td>26247883700</td>\n",
       "      <td>2.694770e+11</td>\n",
       "      <td>-0.004338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>2879.2996</td>\n",
       "      <td>2877.5409</td>\n",
       "      <td>2940.5927</td>\n",
       "      <td>2876.3009</td>\n",
       "      <td>2938.7493</td>\n",
       "      <td>41272341700</td>\n",
       "      <td>4.188720e+11</td>\n",
       "      <td>0.020647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7182 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Preclose       Open    Highest     Lowest      Close  \\\n",
       "Day                                                                 \n",
       "1995-01-03   647.8700   637.7200   647.7100   630.5300   639.8800   \n",
       "1995-01-04   639.8800   641.9000   655.5100   638.8600   653.8100   \n",
       "1995-01-05   653.8100   655.3800   657.5200   645.8100   646.8900   \n",
       "1995-01-06   646.8900   642.7500   643.8900   636.3300   640.7600   \n",
       "1995-01-09   640.7600   637.5200   637.5500   625.0400   626.0000   \n",
       "...               ...        ...        ...        ...        ...   \n",
       "2024-07-25  2901.9518  2891.8918  2897.7674  2872.8497  2886.7416   \n",
       "2024-07-26  2886.7416  2885.9953  2899.1162  2875.3959  2890.8973   \n",
       "2024-07-29  2890.8973  2889.4726  2898.9512  2878.5825  2891.8453   \n",
       "2024-07-30  2891.8453  2885.2152  2885.2152  2865.1493  2879.2996   \n",
       "2024-07-31  2879.2996  2877.5409  2940.5927  2876.3009  2938.7493   \n",
       "\n",
       "                 Volume         Money  Raw_return  \n",
       "Day                                                \n",
       "1995-01-03     23451800  1.806930e+08   -0.012333  \n",
       "1995-01-04     42222000  3.069230e+08    0.021770  \n",
       "1995-01-05     43012300  3.015330e+08   -0.010584  \n",
       "1995-01-06     48748200  3.537580e+08   -0.009476  \n",
       "1995-01-09     50985100  3.985190e+08   -0.023035  \n",
       "...                 ...           ...         ...  \n",
       "2024-07-25  27463950000  2.732820e+11   -0.005241  \n",
       "2024-07-26  27838753600  2.754430e+11    0.001440  \n",
       "2024-07-29  25689972700  2.600950e+11    0.000328  \n",
       "2024-07-30  26247883700  2.694770e+11   -0.004338  \n",
       "2024-07-31  41272341700  4.188720e+11    0.020647  \n",
       "\n",
       "[7182 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new = data['1995-01':'2024-09'].copy()\n",
    "data_new['Close'] = pd.to_numeric(data_new['Close'])\n",
    "data_new['Preclose'] = pd.to_numeric(data_new['Preclose'])\n",
    "# 计算000001上证指数日收益率 两种：\n",
    "data_new['Raw_return'] = data_new['Close'] / data_new['Preclose'] - 1\n",
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raw_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>-0.131631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
       "      <td>-0.023694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>0.177803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-30</th>\n",
       "      <td>-0.103552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>0.207922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>0.008622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-30</th>\n",
       "      <td>0.020932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-31</th>\n",
       "      <td>-0.005801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>-0.038684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-07-31</th>\n",
       "      <td>-0.009656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>355 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Raw_return\n",
       "month                 \n",
       "1995-01-31   -0.131631\n",
       "1995-02-28   -0.023694\n",
       "1995-03-31    0.177803\n",
       "1995-04-30   -0.103552\n",
       "1995-05-31    0.207922\n",
       "...                ...\n",
       "2024-03-31    0.008622\n",
       "2024-04-30    0.020932\n",
       "2024-05-31   -0.005801\n",
       "2024-06-30   -0.038684\n",
       "2024-07-31   -0.009656\n",
       "\n",
       "[355 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Month_data = data_new.resample('ME')['Raw_return'].apply(lambda x: (1+x).prod() - 1).to_frame()\n",
    "Month_data.reset_index(inplace = True)\n",
    "Month_data.rename(columns = {'Day':'month'}, inplace = True)\n",
    "Month_data.set_index('month', inplace = True)\n",
    "Month_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "     <use xlink:href=\"#DejaVuSans-72\" x=\"463.273438\"/>\n",
       "     <use xlink:href=\"#DejaVuSans-6e\" x=\"502.636719\"/>\n",
       "    </g>\n",
       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"pdff4ec8d6a\">\n",
       "   <rect x=\"52.160938\" y=\"21.984375\" width=\"725.4\" height=\"277.2\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 5)) # 图片比例\n",
    "ax.plot(Month_data['Raw_return'],  # 图片数据\n",
    "'.-',  # 线的类型\n",
    "color = 'g',  # 线的颜色 RGB\n",
    "label = 'inflation',  # 图片标签\n",
    "linewidth = 1.5,  # 线的粗细\n",
    "data =Month_data)  # 画图的数据\n",
    "ax.set_title(\"Raw_return\") # 画图的标题\n",
    "ax.set_xlabel('Time') # 画图的x轴名称\n",
    "ax.set_ylabel('Raw_return') # 画图的y轴名称\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>cpi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan-95</td>\n",
       "      <td>129.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb-95</td>\n",
       "      <td>126.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar-95</td>\n",
       "      <td>124.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr-95</td>\n",
       "      <td>123.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-95</td>\n",
       "      <td>123.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Mar-24</td>\n",
       "      <td>99.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Apr-24</td>\n",
       "      <td>100.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>May-24</td>\n",
       "      <td>100.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>Jun-24</td>\n",
       "      <td>100.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>Jul-24</td>\n",
       "      <td>100.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>355 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      month    cpi\n",
       "0    Jan-95  129.5\n",
       "1    Feb-95  126.4\n",
       "2    Mar-95  124.2\n",
       "3    Apr-95  123.4\n",
       "4    May-95  123.1\n",
       "..      ...    ...\n",
       "350  Mar-24   99.8\n",
       "351  Apr-24  100.4\n",
       "352  May-24  100.4\n",
       "353  Jun-24  100.7\n",
       "354  Jul-24  100.9\n",
       "\n",
       "[355 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('D:/ruo-ning/ExportData.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      month    cpi\n",
      "0    Jan-95  129.5\n",
      "1    Feb-95  126.4\n",
      "2    Mar-95  124.2\n",
      "3    Apr-95  123.4\n",
      "4    May-95  123.1\n",
      "..      ...    ...\n",
      "350  Mar-24   99.8\n",
      "351  Apr-24  100.4\n",
      "352  May-24  100.4\n",
      "353  Jun-24  100.7\n",
      "354  Jul-24  100.9\n",
      "\n",
      "[355 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print (data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nhhhai\\n'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>cpi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan-95</td>\n",
       "      <td>129.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb-95</td>\n",
       "      <td>126.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar-95</td>\n",
       "      <td>124.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr-95</td>\n",
       "      <td>123.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-95</td>\n",
       "      <td>123.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Mar-24</td>\n",
       "      <td>99.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Apr-24</td>\n",
       "      <td>100.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>May-24</td>\n",
       "      <td>100.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>Jun-24</td>\n",
       "      <td>100.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>Jul-24</td>\n",
       "      <td>100.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>355 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      month    cpi\n",
       "0    Jan-95  129.5\n",
       "1    Feb-95  126.4\n",
       "2    Mar-95  124.2\n",
       "3    Apr-95  123.4\n",
       "4    May-95  123.1\n",
       "..      ...    ...\n",
       "350  Mar-24   99.8\n",
       "351  Apr-24  100.4\n",
       "352  May-24  100.4\n",
       "353  Jun-24  100.7\n",
       "354  Jul-24  100.9\n",
       "\n",
       "[355 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "hhhai\n",
    "'''\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "time data \"Jan-95\" doesn't match format \"%Y/%m/%d\", at position 0. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m CPI_YoY \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD:/ruo-ning/ExportData.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m CPI_YoY[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(CPI_YoY[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonth\u001b[39m\u001b[38;5;124m'\u001b[39m],\u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY/\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm/\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      3\u001b[0m CPI_YoY\u001b[38;5;241m.\u001b[39mset_index(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonth\u001b[39m\u001b[38;5;124m'\u001b[39m, inplace \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      4\u001b[0m CPI_YoY\u001b[38;5;241m.\u001b[39msort_values(by \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonth\u001b[39m\u001b[38;5;124m'\u001b[39m],axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32md:\\ning\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1067\u001b[0m, in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m   1065\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n\u001b[0;32m   1066\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1067\u001b[0m         values \u001b[38;5;241m=\u001b[39m convert_listlike(arg\u001b[38;5;241m.\u001b[39m_values, \u001b[38;5;28mformat\u001b[39m)\n\u001b[0;32m   1068\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m   1069\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n",
      "File \u001b[1;32md:\\ning\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:433\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    431\u001b[0m \u001b[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[39;00m\n\u001b[0;32m    432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 433\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m    435\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64(\n\u001b[0;32m    436\u001b[0m     arg,\n\u001b[0;32m    437\u001b[0m     dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    441\u001b[0m     allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    442\u001b[0m )\n\u001b[0;32m    444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    445\u001b[0m     \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[0;32m    446\u001b[0m     \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n",
      "File \u001b[1;32md:\\ning\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:467\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[1;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[0;32m    456\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_array_strptime_with_fallback\u001b[39m(\n\u001b[0;32m    457\u001b[0m     arg,\n\u001b[0;32m    458\u001b[0m     name,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    462\u001b[0m     errors: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m    463\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[0;32m    464\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    465\u001b[0m \u001b[38;5;124;03m    Call array_strptime, with fallback behavior depending on 'errors'.\u001b[39;00m\n\u001b[0;32m    466\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 467\u001b[0m     result, tz_out \u001b[38;5;241m=\u001b[39m array_strptime(arg, fmt, exact\u001b[38;5;241m=\u001b[39mexact, errors\u001b[38;5;241m=\u001b[39merrors, utc\u001b[38;5;241m=\u001b[39mutc)\n\u001b[0;32m    468\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m tz_out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    469\u001b[0m         unit \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdatetime_data(result\u001b[38;5;241m.\u001b[39mdtype)[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32mstrptime.pyx:501\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mstrptime.pyx:451\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mstrptime.pyx:583\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime._parse_with_format\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: time data \"Jan-95\" doesn't match format \"%Y/%m/%d\", at position 0. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this."
     ]
    }
   ],
   "source": [
    "CPI_YoY = pd.read_csv('D:/ruo-ning/ExportData.csv')\n",
    "CPI_YoY['month'] = pd.to_datetime(CPI_YoY['month'],format='%Y/%m/%d')\n",
    "CPI_YoY.set_index('month', inplace = True)\n",
    "CPI_YoY.sort_values(by = ['month'],axis=0, ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票名称</th>\n",
       "      <th>股票代码</th>\n",
       "      <th>日期</th>\n",
       "      <th>开盘</th>\n",
       "      <th>收盘</th>\n",
       "      <th>最高</th>\n",
       "      <th>最低</th>\n",
       "      <th>成交量</th>\n",
       "      <th>成交额</th>\n",
       "      <th>振幅</th>\n",
       "      <th>涨跌幅</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3295</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2006-01-12</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.80</td>\n",
       "      <td>5.10</td>\n",
       "      <td>4.33</td>\n",
       "      <td>213621</td>\n",
       "      <td>1.038075e+08</td>\n",
       "      <td>18.03</td>\n",
       "      <td>12.41</td>\n",
       "      <td>0.53</td>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3296</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2006-01-13</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.89</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.77</td>\n",
       "      <td>85845</td>\n",
       "      <td>4.230388e+07</td>\n",
       "      <td>4.79</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.09</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3297</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2006-01-16</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.82</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.79</td>\n",
       "      <td>37701</td>\n",
       "      <td>1.850669e+07</td>\n",
       "      <td>4.29</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3298</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2006-01-17</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.90</td>\n",
       "      <td>4.75</td>\n",
       "      <td>19346</td>\n",
       "      <td>9.328759e+06</td>\n",
       "      <td>3.11</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3299</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2006-01-18</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.99</td>\n",
       "      <td>5.02</td>\n",
       "      <td>4.78</td>\n",
       "      <td>35548</td>\n",
       "      <td>1.751642e+07</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7504</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2023-12-25</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.43</td>\n",
       "      <td>4.46</td>\n",
       "      <td>4.39</td>\n",
       "      <td>159918</td>\n",
       "      <td>7.064810e+07</td>\n",
       "      <td>1.56</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7505</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2023-12-26</td>\n",
       "      <td>4.42</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.42</td>\n",
       "      <td>4.33</td>\n",
       "      <td>163063</td>\n",
       "      <td>7.134829e+07</td>\n",
       "      <td>2.03</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7506</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>4.46</td>\n",
       "      <td>4.55</td>\n",
       "      <td>4.78</td>\n",
       "      <td>4.43</td>\n",
       "      <td>487393</td>\n",
       "      <td>2.214166e+08</td>\n",
       "      <td>8.03</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.19</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7507</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>4.55</td>\n",
       "      <td>4.55</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.40</td>\n",
       "      <td>322643</td>\n",
       "      <td>1.453550e+08</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7508</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.59</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.52</td>\n",
       "      <td>221839</td>\n",
       "      <td>1.018295e+08</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4214 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      股票名称  股票代码          日期    开盘    收盘    最高    最低     成交量           成交额  \\\n",
       "3295  深振业Ａ     6  2006-01-12  4.35  4.80  5.10  4.33  213621  1.038075e+08   \n",
       "3296  深振业Ａ     6  2006-01-13  4.80  4.89  5.00  4.77   85845  4.230388e+07   \n",
       "3297  深振业Ａ     6  2006-01-16  4.87  4.82  5.00  4.79   37701  1.850669e+07   \n",
       "3298  深振业Ａ     6  2006-01-17  4.80  4.80  4.90  4.75   19346  9.328759e+06   \n",
       "3299  深振业Ａ     6  2006-01-18  4.80  4.99  5.02  4.78   35548  1.751642e+07   \n",
       "...    ...   ...         ...   ...   ...   ...   ...     ...           ...   \n",
       "7504  深振业Ａ     6  2023-12-25  4.45  4.43  4.46  4.39  159918  7.064810e+07   \n",
       "7505  深振业Ａ     6  2023-12-26  4.42  4.36  4.42  4.33  163063  7.134829e+07   \n",
       "7506  深振业Ａ     6  2023-12-27  4.46  4.55  4.78  4.43  487393  2.214166e+08   \n",
       "7507  深振业Ａ     6  2023-12-28  4.55  4.55  4.58  4.40  322643  1.453550e+08   \n",
       "7508  深振业Ａ     6  2023-12-29  4.53  4.59  4.64  4.52  221839  1.018295e+08   \n",
       "\n",
       "         振幅    涨跌幅   涨跌额    换手率  \n",
       "3295  18.03  12.41  0.53  11.28  \n",
       "3296   4.79   1.88  0.09   4.53  \n",
       "3297   4.29  -1.43 -0.07   1.99  \n",
       "3298   3.11  -0.41 -0.02   1.02  \n",
       "3299   5.00   3.96  0.19   1.88  \n",
       "...     ...    ...   ...    ...  \n",
       "7504   1.56  -1.12 -0.05   1.18  \n",
       "7505   2.03  -1.58 -0.07   1.21  \n",
       "7506   8.03   4.36  0.19   3.61  \n",
       "7507   3.96   0.00  0.00   2.39  \n",
       "7508   2.64   0.88  0.04   1.64  \n",
       "\n",
       "[4214 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['日期']>'2005/8/15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票名称</th>\n",
       "      <th>股票代码</th>\n",
       "      <th>日期</th>\n",
       "      <th>开盘</th>\n",
       "      <th>收盘</th>\n",
       "      <th>最高</th>\n",
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       "      <th>成交额</th>\n",
       "      <th>振幅</th>\n",
       "      <th>涨跌幅</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>换手率</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>1992-04-27</td>\n",
       "      <td>33.00</td>\n",
       "      <td>38.50</td>\n",
       "      <td>40.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>131</td>\n",
       "      <td>2535000.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>285.00</td>\n",
       "      <td>28.50</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>1992-04-28</td>\n",
       "      <td>38.00</td>\n",
       "      <td>39.15</td>\n",
       "      <td>40.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>430</td>\n",
       "      <td>8472000.0</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>1992-04-29</td>\n",
       "      <td>39.15</td>\n",
       "      <td>39.35</td>\n",
       "      <td>39.80</td>\n",
       "      <td>38.50</td>\n",
       "      <td>349</td>\n",
       "      <td>6841000.0</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>1992-04-30</td>\n",
       "      <td>39.35</td>\n",
       "      <td>39.95</td>\n",
       "      <td>40.50</td>\n",
       "      <td>39.00</td>\n",
       "      <td>358</td>\n",
       "      <td>7100000.0</td>\n",
       "      <td>3.81</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>1992-05-03</td>\n",
       "      <td>40.30</td>\n",
       "      <td>41.40</td>\n",
       "      <td>41.50</td>\n",
       "      <td>40.10</td>\n",
       "      <td>208</td>\n",
       "      <td>4261000.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3827</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2008-04-16</td>\n",
       "      <td>20.80</td>\n",
       "      <td>19.85</td>\n",
       "      <td>20.80</td>\n",
       "      <td>19.61</td>\n",
       "      <td>20346</td>\n",
       "      <td>40877248.0</td>\n",
       "      <td>5.72</td>\n",
       "      <td>-4.57</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3829</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2008-04-21</td>\n",
       "      <td>20.15</td>\n",
       "      <td>17.19</td>\n",
       "      <td>20.66</td>\n",
       "      <td>17.19</td>\n",
       "      <td>61434</td>\n",
       "      <td>112421008.0</td>\n",
       "      <td>18.17</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>-1.91</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3832</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2008-04-24</td>\n",
       "      <td>22.63</td>\n",
       "      <td>22.63</td>\n",
       "      <td>22.63</td>\n",
       "      <td>22.00</td>\n",
       "      <td>149480</td>\n",
       "      <td>338039104.0</td>\n",
       "      <td>3.06</td>\n",
       "      <td>10.01</td>\n",
       "      <td>2.06</td>\n",
       "      <td>6.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3833</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2008-04-25</td>\n",
       "      <td>23.28</td>\n",
       "      <td>24.89</td>\n",
       "      <td>24.89</td>\n",
       "      <td>22.71</td>\n",
       "      <td>182834</td>\n",
       "      <td>440817792.0</td>\n",
       "      <td>9.63</td>\n",
       "      <td>9.99</td>\n",
       "      <td>2.26</td>\n",
       "      <td>7.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3834</th>\n",
       "      <td>深振业Ａ</td>\n",
       "      <td>6</td>\n",
       "      <td>2008-04-28</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.30</td>\n",
       "      <td>26.28</td>\n",
       "      <td>24.11</td>\n",
       "      <td>198842</td>\n",
       "      <td>505085600.0</td>\n",
       "      <td>8.72</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.41</td>\n",
       "      <td>8.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>770 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      股票名称  股票代码          日期     开盘     收盘     最高     最低     成交量          成交额  \\\n",
       "0     深振业Ａ     6  1992-04-27  33.00  38.50  40.00  33.00     131    2535000.0   \n",
       "1     深振业Ａ     6  1992-04-28  38.00  39.15  40.00  38.00     430    8472000.0   \n",
       "2     深振业Ａ     6  1992-04-29  39.15  39.35  39.80  38.50     349    6841000.0   \n",
       "3     深振业Ａ     6  1992-04-30  39.35  39.95  40.50  39.00     358    7100000.0   \n",
       "4     深振业Ａ     6  1992-05-03  40.30  41.40  41.50  40.10     208    4261000.0   \n",
       "...    ...   ...         ...    ...    ...    ...    ...     ...          ...   \n",
       "3827  深振业Ａ     6  2008-04-16  20.80  19.85  20.80  19.61   20346   40877248.0   \n",
       "3829  深振业Ａ     6  2008-04-21  20.15  17.19  20.66  17.19   61434  112421008.0   \n",
       "3832  深振业Ａ     6  2008-04-24  22.63  22.63  22.63  22.00  149480  338039104.0   \n",
       "3833  深振业Ａ     6  2008-04-25  23.28  24.89  24.89  22.71  182834  440817792.0   \n",
       "3834  深振业Ａ     6  2008-04-28  25.00  25.30  26.28  24.11  198842  505085600.0   \n",
       "\n",
       "         振幅     涨跌幅    涨跌额   换手率  \n",
       "0     70.00  285.00  28.50  0.09  \n",
       "1      5.19    1.69   0.65  0.28  \n",
       "2      3.32    0.51   0.20  0.23  \n",
       "3      3.81    1.52   0.60  0.23  \n",
       "4      3.50    3.63   1.45  0.14  \n",
       "...     ...     ...    ...   ...  \n",
       "3827   5.72   -4.57  -0.95  0.88  \n",
       "3829  18.17  -10.00  -1.91  2.66  \n",
       "3832   3.06   10.01   2.06  6.46  \n",
       "3833   9.63    9.99   2.26  7.90  \n",
       "3834   8.72    1.65   0.41  8.60  \n",
       "\n",
       "[770 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['开盘']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # 画图板块\n",
    "import matplotlib.dates as mdates # 日期格式处理模块\n",
    "from matplotlib import style # 用来自定义图标风格的模块\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg' # 提高分辨率\n",
    "# 使用中文字体的操作\n",
    "# from matplotlib.font_manager import FontProperties # 画图自定义字体\n",
    "# from pylab import mpl # 画图自定义字体\n",
    "# mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体\n",
    "# mpl.rcParams['font.family'] = 'Times New Roman' # 指定默认字体"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "time data \"1992-04-27\" doesn't match format \"%Y/%m/%d\", at position 0. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m日期\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m日期\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY/\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm/\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      2\u001b[0m data\u001b[38;5;241m.\u001b[39mset_index(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m日期\u001b[39m\u001b[38;5;124m'\u001b[39m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      3\u001b[0m data\u001b[38;5;241m.\u001b[39msort_values(by \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m日期\u001b[39m\u001b[38;5;124m'\u001b[39m], ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32md:\\ning\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1067\u001b[0m, in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m   1065\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n\u001b[0;32m   1066\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1067\u001b[0m         values \u001b[38;5;241m=\u001b[39m convert_listlike(arg\u001b[38;5;241m.\u001b[39m_values, \u001b[38;5;28mformat\u001b[39m)\n\u001b[0;32m   1068\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m   1069\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n",
      "File \u001b[1;32md:\\ning\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:433\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    431\u001b[0m \u001b[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[39;00m\n\u001b[0;32m    432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 433\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m    435\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64(\n\u001b[0;32m    436\u001b[0m     arg,\n\u001b[0;32m    437\u001b[0m     dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    441\u001b[0m     allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    442\u001b[0m )\n\u001b[0;32m    444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    445\u001b[0m     \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[0;32m    446\u001b[0m     \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n",
      "File \u001b[1;32md:\\ning\\Lib\\site-packages\\pandas\\core\\tools\\datetimes.py:467\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[1;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[0;32m    456\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_array_strptime_with_fallback\u001b[39m(\n\u001b[0;32m    457\u001b[0m     arg,\n\u001b[0;32m    458\u001b[0m     name,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    462\u001b[0m     errors: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m    463\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[0;32m    464\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    465\u001b[0m \u001b[38;5;124;03m    Call array_strptime, with fallback behavior depending on 'errors'.\u001b[39;00m\n\u001b[0;32m    466\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 467\u001b[0m     result, tz_out \u001b[38;5;241m=\u001b[39m array_strptime(arg, fmt, exact\u001b[38;5;241m=\u001b[39mexact, errors\u001b[38;5;241m=\u001b[39merrors, utc\u001b[38;5;241m=\u001b[39mutc)\n\u001b[0;32m    468\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m tz_out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    469\u001b[0m         unit \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdatetime_data(result\u001b[38;5;241m.\u001b[39mdtype)[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32mstrptime.pyx:501\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mstrptime.pyx:451\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mstrptime.pyx:583\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime._parse_with_format\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: time data \"1992-04-27\" doesn't match format \"%Y/%m/%d\", at position 0. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this."
     ]
    }
   ],
   "source": [
    "data['日期'] = pd.to_datetime(data['日期'], format='%Y/%m/%d')\n",
    "data.set_index('日期', inplace=True)\n",
    "data.sort_values(by = ['日期'], ascending=True)\n",
    "data['收盘'] = pd.to_numeric(data['收盘'],errors='coerce')\n",
    "data['Preclose'] = data['收盘'].shift(1)\n",
    "data['成交额'] = data['收盘'] / data['Preclose'] - 1\n",
    "data"
   ]
  },
  {
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